Mining IPTV User Behaviors with an Enhanced LDA Model

Abstract

With the increasing popularity of IPTV industry, QoE has been regarded as one of the most promising evaluation indicators for IPTV service. However, due to the increasing amount of TV programs and users' mixed preferences, how to recommend interesting programs for users is still a challenging and urgent problem. Existing related researches ignore the personalized recommendation and the prediction of prospective interests for different users from large amounts of TV programs. To solve this problem, this work proposes an enhanced latent Dirichlet allocation (LDA) model to analyze user behaviors and recommend personalized programs of the users' mixed interests. Specifically, we put forward a new attribute called viewing ratio to calculate the proportion of program's time viewed by the user, which could measure users' subjective viewing experience from objective indicators. Based on the proposed model, we improve the accuracy of user behaviors modeling and prediction of prospective interests. Experimental results show that our model has better performances of programs recommendation and TV viewing experience than other models.